class_docstr: Actor network. The network follows the standard actor-critic architecture used in Deep Reinforcement Learning.
  The model is used in Counterfactual with Reinforcement Learning (CFRL) for both data modalities (images and
  tabular). The hidden dimension used for the all experiments is 256, which is a common choice in most benchmarks.
docstr: Forward pass
  
  Parameters
  ----------
  x
      Input tensor.
  
  Returns
  -------
  Continuous action.
docstr_tok: ["Forward","pass","Parameters","--","--","--","--","--","x","Input","tensor",".","Returns","--","--","--","-","Continuous","action","."]
code: def forward(self, x: torch.Tensor) -> torch.Tensor:
      """
      Forward pass
  
      Parameters
      ----------
      x
          Input tensor.
  
      Returns
      -------
      Continuous action.
      """
      x = F.relu(self.ln1(self.fc1(x)))
      x = F.relu(self.ln2(self.fc2(x)))
      x = torch.tanh(self.fc3(x))
      return x
  
file_path: alibi/alibi/models/pytorch/actor_critic.py
imports: import torch
  import torch.nn
  import torch.nn.functional
label_desc: Use image node_id 2 for calling the Actor obj's underlying member method code with example usage: obj.forward(x) and returns: x